As electronic devices become ubiquitous, several applications demand signal processing and transmission with as little power dissipation as possible. For example, wireless sensor networks consist of a number of sensor nodes that sense, process, transmit, and receive information wirelessly; they must often do so under severe constraints in terms of energy usage, whether such energy is derived from a small, difficult-to-replace battery, or through energy harvesting techniques. In such applications, it is important to minimize the power budget in sampling, processing, and transmission of data. In some cases, communication may take place locally, for example, from sensor node to sensor node in a network, and then the power budget at the receiving end is important as well.
Conventional sampling and processing occur at a fixed sampling rate, as dictated by the Shannon theorem, which states that the samples of a band-limited signal, taken at a rate twice its highest frequency component, are sufficient to completely represent and reconstruct the signal from the samples. The sampling frequency in fixed sampling rate systems is set according to this principle. However, some signals have frequency spectra that change significantly with time. When the signal frequency decreases, the above uniform sampling rate needlessly wastes samples and results in wasted energy in processing, transmission, and reception. Accordingly, there is a need for non-uniform sampling techniques that address this problem.